How to Build an MCP Server (Playbook)

The Model Context Protocol gives AI clients and agents a standard way to reach data and actions, which means integrations built as MCP servers can be reused across clients rather than rebuilt per application. It also means an MCP server is an API that models call, with all the security and contract implications that carries. This playbook covers the build, following FISTA's AI agents practice. Concepts are in what is model context protocol and how to build tool use for llm agents.

What does an MCP server do?

Element	Function
Resources	Readable content such as documents, records, and search results with permission-aware access
Tools	Callable actions and queries with schemas and consequence levels
Prompts	Reusable prompt templates the client can invoke
Transport and auth	Protocol handling, client authentication, user identity
Mediation	Validation, scoping, policy, logging, sandboxing
Observability	Invocation logs, latency, errors, usage by client and user

Step 1: Decide what to expose

Inventory what agents legitimately need from the system: which records to read, which searches to run, which actions to take. Design curated resources and tools for those needs rather than mirroring the underlying API. Assign a consequence level to each tool. Write the contracts as the specification. Contract design follows how to build tool use for llm agents.

Step 2: Design schemas

Give each tool a strict input schema with types, constraints, and examples, and an output schema; give resources stable identifiers and metadata. Descriptions must let a model choose correctly among tools. Ambiguity here produces wrong calls. See what is structured output.

Step 3: Implement authentication and scoping

Authenticate clients; resolve the end user's identity through delegated authorization; scope every resource read and tool call to that user's permissions in the underlying system; and never rely on the model's stated intent as authorization. Issue short-lived credentials from a secrets manager. Design is in ai access control and ai secrets management.

Step 4: Validate and mediate

Validate all inputs against schemas; enforce rate limits, value caps, and allowed targets; route consequential tools to approval flows where the client supports them or refuse them where it does not; and sandbox any code or file execution with egress restrictions. Policy is enforced in the server, not assumed of the client. The security model is in the AI agent security architecture whitepaper.

Step 5: Return context-sized results and structured errors

Return results conforming to output schemas and sized for model context, with pagination or references for large data, and errors that are structured and informative, distinguishing retryable from terminal. Content returned from resources may contain injection attempts; label it as data and consider sanitization. See prompt injection defense checklist.

Step 6: Observe

Log every resource read and tool call with client, user, arguments (redacted), result summary, latency, and outcome; expose usage metrics by client and tool; and alert on anomalies such as scope-escalation attempts or unusual volumes. See the AI observability whitepaper.

Step 7: Test

Unit-test handlers including validation and error paths; run protocol contract tests; run adversarial tests for injection through arguments and returned content and for scope escalation; and evaluate trajectories with real AI clients in a simulated environment. Harness design is in how to build an agent evaluation harness.

Step 8: Operate and govern

Version the server and its contracts; publish changes with notice to consuming teams; review exposed tools and permissions on a schedule; and maintain an inventory of servers your organization runs and consumes, pinned and reviewed as supply chain. Governance is in ai supply chain security.

Worked example: an MCP server for a ticketing system

An IT team exposes its ticketing system to internal AI assistants through an MCP server. Resources include ticket records and knowledge articles, scoped to the requesting user's visibility. Tools include ticket search, ticket creation with a validated schema, comment addition, and status change, with status change classified as a reversible write and closure requiring the assignee's confirmation through the client's approval flow. Clients authenticate with organizational identity, and each call carries the end user's delegated authorization. Inputs are validated; results are paginated; knowledge article content is labeled as data. Logs record every call by user and tool. Adversarial tests confirm a crafted ticket description cannot trigger status changes through an assistant, and trajectory evaluation confirms assistants select the right tools and respect confirmation. Two assistants adopt the server within weeks rather than each building a ticketing integration.

What does it cost?

An MCP server is standard service engineering with additional security and contract work; its value multiplies with each client that reuses it. See ai integration cost.

What are the common mistakes?

• Exposing raw APIs or database access as tools.
• Trusting the client to enforce user permissions.
• Vague tool descriptions that cause wrong calls.
• Returning huge results that overflow context.
• Treating returned content as trusted.
• Consuming third-party servers without review or pinning.

How do you phase the server?

Expose read resources and search tools first so consuming assistants deliver value while the authentication, scoping, and logging are proven under real use. Add write tools with validation and idempotency once read paths are stable, and add consequential tools only where clients support confirmation flows. Publish the server to a second client before broadening scope, because the second consumer reveals contract ambiguities the first one worked around.

How do you version an MCP server?

Tools and resources are contracts that AI clients depend on. Version the server, keep old tool signatures available through a deprecation window, announce changes to consuming teams, and test every client integration against new versions before switching. A silent signature change breaks agents in ways that look like model failures.

How FISTA Solutions builds MCP servers

FISTA Solutions builds MCP servers to this playbook: curated resources and tools with strict schemas and consequence levels, client authentication with delegated user scoping, validation and policy in the server, sandboxed execution, context-sized results, full observability, three-level testing, and supply-chain governance. The AI agents practice consumes them in the agents it delivers, AI enablement builds the shared servers and registry, and forward deployed engineers integrate them with your systems and identity provider. The record behind the work is 150+ projects with 99.9% uptime.

To scope an MCP server, message FISTA on WhatsApp, or read how to build an ai api for the general API design foundation.